基于动态NDSI阈值的每日积雪监测方法

doi:10.12082/dqxxkx.2020.190139

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (2): 298-307.doi: 10.12082/dqxxkx.2020.190139

基于动态NDSI阈值的每日积雪监测方法

孙玉燕^1,², 张磊^1,^*(), 卢善龙¹, 刘红超^1,²

^1. 中国科学院空天信息创新研究院数字地球重点实验室,北京 100094
^2. 中国科学院大学电子电气与通信工程学院,北京 100049

收稿日期:2019-03-26 修回日期:2019-09-05 出版日期:2020-02-25 发布日期:2020-04-13
通讯作者: 张磊 E-mail:zhanglei@radi.ac.cn
作者简介:孙玉燕（1994— ）,女,山东潍坊人,硕士生,主要从事季节性积雪监测研究。E-mail: Sunyy@radi.ac.cn
基金资助:
国家重点研发计划项目(2017YFC0405802)

Method for Monitoring Daily Snow Cover based on Dynamic NDSI Thresholds

SUN Yuyan^1,², ZHANG Lei^1,^*(), LU Shanlong¹, LIU Hongchao^1,²

^1. Key Laboratory of Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
^2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-03-26 Revised:2019-09-05 Online:2020-02-25 Published:2020-04-13
Contact: ZHANG Lei E-mail:zhanglei@radi.ac.cn
Supported by:
National Key Research and Development Program of China(2017YFC0405802)

摘要/Abstract

摘要：

准确掌握积雪覆盖信息对于气象、水文和全球气候变化研究都具有重要的意义。遥感技术在进行大范围、高频率的积雪覆盖监测中发挥着重要的作用。目前,SNOMAP算法是用于积雪遥感监测最普遍的技术手段,其核心是利用固定阈值的归一化差分积雪指数（Normalized Difference Snow Index,NDSI）进行积雪识别,但这种方法忽略了积雪光谱信息的时相变化,会产生积雪监测的误差。本文提出了一种动态NDSI阈值方法,以纯永久积雪像元的平均NDSI值作为参照系调整固定的NDSI阈值,从而削减影像光谱值波动对积雪识别的影响。以三江源地区作为研究区域,将基于每日MODIS数据进行积雪监测最佳的NDSI阈值与同日纯永久积雪像元的平均NDSI值作线性回归,通过每日纯永久积雪像元平均NDSI值的变化来调整用于积雪识别的NDSI阈值。结果表明：① 基于每日MODIS数据进行积雪覆盖监测最佳的NDSI阈值与同日纯永久积雪像元的平均NDSI值之间存在较好的线性关系,决定系数R ²达到0.86;② 三江源地区动态NDSI阈值的范围为0.29~0.37,其平均值在0.33左右,说明MODIS全球积雪面积产品中将NDSI阈值取为0.40会低估三江源地区的积雪面积;③ 与采用固定NDSI阈值0.33的监测方法相比,动态NDSI阈值法近似率、总体分类精度和F值的平均值分别提高了5.17%、0.70%、1.14%。

关键词: 积雪监测, 动态NDSI, 阈值, 纯永久积雪, 时相变化, 监测精度, 三江源

Abstract:

Accurate snow cover information is of great significance to the study of meteorology, hydrology, and global climate change. Remote sensing techniques play an important role in large-scale and high-frequency snow cover monitoring. Nowadays, SNOMAP algorithm is the most common method for remote sensing monitoring of snow, which mainly uses fixed NDSI (Normalized Difference Snow Index) thresholds to identify snow. However, this method ignores the temporal variations of snow spectral information, leading to monitoring errors of snow cover. In this study, we proposed an adjusted method to monitor snow cover by dynamic NDSI thresholds. This method adjusts fixed NDSI thresholds by using the average NDSI value of pure permanent snow as reference to reduce the influence of spectral fluctuations. Snow cover in the Sanjiangyuan area was identified and monitored by this method. There were four steps: (1) OLI and MODIS data of the same region, the same period and cloud-free were selected. The OLI NDSI threshold of the best snow cover recognition was determined by human-computer interaction. (2) The snow area monitored based on OLI data was used as the true value of the ground to calibrate the optimal MODIS NDSI threshold on the same day. (3) The average NDSI value of the pure permanent snow in the Sanjiangyuan area on the same day was counted. The elevation of the pure permanent snow pixels was more than 5800 meters and the FSC (Fractional Snow Cover) of them was 100%. (4) The functional relationship between the optimal MODIS NDSI threshold and the average NDSI value of the pure permanent snow was established. The dynamic MODIS NDSI threshold was obtained by the linear regression and varied with the average NDSI value of pure permanent snow. Results show that: (1) Based on daily MODIS data, there was a good linear relationship between the optimal NDSI threshold for snow cover monitoring and the average NDSI value of pure permanent snow on the same day, and the determinant coefficient R ² reached 0.86. (2) The dynamic NDSI thresholds of Sanjiangyuan area were between 0.29 and 0.37, and the average value of NDSI threshold was about 0.33, indicating that 0.40 as the NDSI threshold would underestimate the snow cover area of the Sanjiangyuan area. (3) The average values of the approximation ratio, the overall classification accuracy, and F of dynamic NDSI threshold method were 96.61%, 94.62%, and 91.99%, respectively. Compared with the monitoring method with the fixed NDSI threshold of 0.33, they were improved by 5.17%, 0.70%, and 1.14%, respectively. Our findings demonstrate the effectiveness of the proposed method.

Key words: snow monitoring, dynamic NDSI, threshold, pure permanent snow, temporal variation, monitoring accuracy, Sanjiangyuan

孙玉燕, 张磊, 卢善龙, 刘红超. 基于动态NDSI阈值的每日积雪监测方法[J]. 地球信息科学学报, 2020, 22(2): 298-307.DOI:10.12082/dqxxkx.2020.190139

SUN Yuyan, ZHANG Lei, LU Shanlong, LIU Hongchao. Method for Monitoring Daily Snow Cover based on Dynamic NDSI Thresholds[J]. Journal of Geo-information Science, 2020, 22(2): 298-307.DOI:10.12082/dqxxkx.2020.190139

图/表 6

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编号	成像日期	行列号	编号	成像日期	行列号
01	2013-11-04	139036	02	2013-11-06	137035
03	2013-11-15	136035	04	2013-11-22	137036
05	2013-11-29	138036	06	2013-12-01	136037
07	2013-12-03	134036	08	2013-12-08	137035
09	2013-12-28	133036	10	2014-10-31	138036
11	2014-11-09	137035	12	2014-11-13	133036
13	2014-11-16	138035	14	2014-12-02	138035
15	2014-12-29	135037	16	2014-12-31	133036
17	2015-01-09	132036	18	2015-02-06	136037
19	2015-01-21	136037	20	2015-03-21	133036
21	2015-04-22	133035	22	2015-11-16	133036
23	2015-11-28	137036	24	2016-01-08	136036
25	2016-01-17	135037	26	2016-02-04	133036
27	2016-10-17	133036	28	2016-10-22	136037
29	2016-10-31	135037	30	2016-12-04	133037
31	2017-01-01	137036	32	2017-01-05	133036
33	2017-02-04	135037	34	2017-04-30	138036
35	2017-10-27	134036	36	2017-11-01	137036
37	2017-11-05	133036	38	2017-11-08	138036
39	2017-11-12	134036	40	2017-11-19	135036
41	2017-12-30	134035	42	2018-01-01	132036
43	2018-03-29	133036	44	2018-11-07	133037
45	2018-11-21	135037	46	2018-11-23	133036
47	2018-12-25	133036	48	2018-11-30	134036
49	2018-12-16	134035	50	2019-01-01	134036

时间	精度指标	监测方法		动态比固定NDSI阈值提高精度
时间	精度指标	动态NDSI阈值	固定NDSI阈值0.33	动态比固定NDSI阈值提高精度
2013-11-04	近似率	97.58	82.45	15.13
	总体分类精度	94.59	93.71	0.88
	F值	87.05	83.30	3.75
2013-11-22	近似率	85.79	71.42	14.37
	总体分类精度	90.95	88.33	2.62
	F值	79.23	74.18	5.05
2013-12-03	近似率	96.66	93.94	2.72
	总体分类精度	92.07	91.75	0.32
	F值	87.85	87.55	0.30
2014-12-02	近似率	97.42	97.42	0.00
	总体分类精度	95.85	95.85	0.00
	F值	92.57	92.57	0.00
2015-11-16	近似率	98.01	94.64	3.37
	总体分类精度	95.10	94.63	0.47
	F值	96.58	96.32	0.26
2016-10-31	近似率	98.68	94.35	4.33
	总体分类精度	98.83	98.15	0.68
	F值	99.31	98.90	0.41
2017-10-27	近似率	94.59	92.55	2.04
	总体分类精度	93.58	93.12	0.46
	F值	93.23	92.67	0.56
2017-11-01	近似率	99.35	92.31	7.04
	总体分类精度	92.30	91.85	0.45
	F值	89.33	89.00	0.33
2017-11-12	近似率	98.56	97.98	0.58
	总体分类精度	96.84	96.70	0.14
	F值	96.93	96.80	0.13
2018-11-23	近似率	99.49	97.34	2.15
	总体分类精度	96.13	95.15	0.98
	F值	97.84	97.25	0.59

基于动态NDSI阈值的每日积雪监测方法

Method for Monitoring Daily Snow Cover based on Dynamic NDSI Thresholds

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